1. Field of the Invention
The present invention relates to an object tracking apparatus configured to track an object on image data captured continuously.
2. Description of the Related Art
In an image surveillance system or a video conference system, a camera system using images captured by a camera in various situations has been put into practical use. The camera system has a tracking function that automatically tracks an object concerned and captures the object while changing a capture region. For example, in the image surveillance system with the tracking function, when a suspicious individual is set as an object, the system tracks the suspicious individual and continues to capture him/her as surveillance images. Also, in the video conference system with the tracking function, when a speaker is set as an object, the system tracks the speaker and continues to capture him/her as conference images.
When a system continues to capture an object as images, there is a need for controlling a pan, a tilt, a zoom factor and the like of a camera according to movement of the object, in order to keep the object within angles of view of the camera. Therefore, the system has to recognize the object on each image and detect a moving direction of the object.
As a method for recognizing an object on an image and detecting a moving direction of the object, a background difference method or a frame difference method using luminance difference has been employed. Recently, an object tracking technique using a particle filter is studied as disclosed in a Patent document 1 (Japanese Unexamined Patent Application Publication No. 2004-282535) and non Patent documents 1 (M. Isard, A. Blake: CONDENSATION—Conditional Density Propagation for Visual Tracking, Int. J. Computer Vision, vol. 28, No. 1, pp. 5-28 (1998)) and 2 (Hironobu Fujiyoshi: Moving Image Concept Technique and Application Thereof, text of Fujiyoshi Lab, Department of Computer Science, College of Engineering, Chubu University, pp. 76-80 (2007)).
The particle filter is an approximative calculation method of Bayesian filtering using posterior probability. The particle filter describes a probability distribution function by a finite number of particles and makes a time-series prediction using the probability distribution function. Namely, the particle filter can be said to be Sequential Monte Carlo method based on sampling. Even if a distribution in time-series is not approximated by Gaussian distribution, the particle filter can trace an object concerned because it approximates the distribution in time-series by sets of positions and weights of particles. As described in the Patent document 1, when the particle filter is applied to a trace of an object, likelihood is calculated using a color of the object. In this case, the likelihood is calculated using an existence rate of pixels of color close to the color of the object in the vicinity of each particle, and then a position of the object is estimated based on the result of calculation.
As described above, in an object tracking process using the particle filter, an object is traced by setting a color of the object and increasing likelihood of particles arranged in the vicinity of the object.
However, since a color of an object on an output image from a camera is easily changed due to an adjustment for the object by the camera such as a white balance adjustment or an exposure adjustment, it is often the case that the color of the object on the output image differs from a real color of the object. Also, the color of the object on the output image is changed due to a degree of exposure to light or a degree of darkness by shadow with respect to the object. Therefore, in the object tracking process, even if such a color change occurs, it is required that accuracy of tracking the object is kept.
The Patent document 1 describes that the object tracking process adjusts to a color change of an object due to an illumination condition and the like by relearning a color of the object. However, when a color of object is changed, the object tracking process can not always keep accuracy of tracking the object because it merely updates the color of object on each frame. Especially, when the color of object is sharply changed, the object tracking process can not keep accuracy of tracking the object.